The growing demand for data scientists presents an enticing career path for students and professionals. It also includes those who are not data scientists but are fascinated by data and data science, which has left them wondering what data science skills and big data skills are required for careers in data science.  

Data scientists are in high demand at the enterprise level across all industry verticals as Big Data is used as an insight-generating engine. To improve the process of product development, enhance customer retention, or find new business opportunities, organizations are increasingly relying on data scientists to sustain, grow, and stay one step ahead of the competition.

Data Scientist Skills:

There are two types of important skills:

• Technical skills
• Non-technical skills

An overview of the technical skills required to become a Data Scientist:

• Computing and statistical analysis
• Artificial Intelligence
• Machine Learning
• Analysis of large datasets
• Visualization of data
• Data Wrangling
• Mathematics
• Programming
• Statistics
• Big Data

Data scientists need additional technical skills, such as:

Skill #1- Programming:

You will need to have experience with various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python being the most common coding language required in data science roles. Data scientists use these programming languages to organize unstructured data sets.

Skill #2- Knowledge of SAS and Other Analytical Tools:

Understanding analytical tools can help data scientists extract valuable information from organized data sets. SAS, Hadoop, Spark, Hive, Pig, and R are the most popular data analytics tools. You can gain valuable data scientist skills and establish your expertise in these tools by obtaining certifications.

Skill #3- Work well with unstructured data:

Ideally, data scientists should have experience with unstructured data coming from a variety of sources and channels. A data scientist, for example, should have experience handling social media as well if they are working on a project to help the marketing team provide insightful research.

Data scientists need these non-technical skills:

We are now shifting our focus from technical skills to non-technical skills required for becoming a data scientist. Personal skills are hard to assess based solely on educational qualifications, certifications, and so on. Examples include:

Skill #1- A Strong Business Acumen:

Business acumen is the best way to channel technical skills productively. If not, an aspiring data scientist may not discern the problems and potential challenges that need to be solved for an organization to grow. It is crucial to your organization’s exploration of new business opportunities for this purpose.

Skill #2 – Strong Communication Skills:

The next top data scientist skill is communication. They know how to extract, understand, and analyze data. To be successful in your role and benefit your organization, you should communicate your findings with team members who do not share your background.

Skill #3 – Great Data Intuition:

Undoubtedly, this is one of the most important non-technical skills for a data scientist. Data insights in large datasets are not always apparent, and a data scientist with intuition knows when to look beyond the surface for insights. 

To gain this skill, data scientists must have the proper training and experience. Data scientist skills are learned through experience, and boot camps are a great way to hone them.

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